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Text Mining Customer Reviews for Aspect-Based Restaurant Rating


Affiliations
1 University of San Jose-Recoletos, Cebu City, Philippines
 

This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.

Keywords

Text Mining, Sentiment Analysis, Natural Language Processing, Aspect-Based Scoring.
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  • C. C. Aggarwal and C. Zhai, Mining Text Data, Springer Science & Business Media, 2012.

  • L. Jack and Y. Tsai, "Using Text Mining of Amazon Reviews to Explore," in The 2015 International Conference on Data Mining, Las Vegas, Nevada, USA, 2015.

  • F. V. Ordenes, ,. B. Theodoulidis, J. Burton, T. Gruber and M. Zaki, "Analyzing Customer Experience Feedback Using Text Mining A Linguistics-Based Approach," Journal of Service Research, vol. 17, no. 4, 2014.

  • V. Suresh, S. Roohi and M. Eirinaki, "Aspect-Based Opinion Mining and Recommendation System for Restaurant Reviews," in ACM RecSys'14, 2014.

  • M. Hu and B. Liu, "Mining Opinion Features in Customer Reviews," AAAI, vol. 4, no. 4, 2004.

  • G. Somprasertsri and P. Lalitrojwong, "Mining Feature-Opinion in Online Customer Reviews for," Journal of Universal Computer Science,, vol. 16, no. 6, pp. 938-955, 2010.

  • M. Hu and B. Liu, Mining and Summarizing Customer Reviews, in KDD ’04 : Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168– 177, New York, NY, USA, 2004, ACM.

  • A. Gupta, T. Tenneti and A. Gupta. Sentiment based Summarization of Restaurant Reviews, June 3, 2009.

  • Chinsa T C, Shibily J. Aspect based Opinion Mining from Restaurant Reviews, International Journal of Computer Applications (0975 – 8887). Advanced Computing and Communication Techniques for High Performance Applications (ICACCTHPA-2014) , 2014.

  • Titov, Ivan, and Ryan McDonald. "A joint model of text and aspect ratings for sentiment summarization." proceedings of ACL-08: HLT,308-316, 2008

  • Samaneh Moghaddam and Martin Ester.. Opinion digger: an unsupervised opinion miner from unstructured product reviews. In Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10). ACM, New York, NY, USA, 1825-1828. 2010 DOI=http://dx.doi.org/10.1145/1871437.1871739

  • I. B. Weiner, Handbook of Psychology, Research Methods in Psychology, John Wiley & Sons,, 2003.


Abstract Views: 276

PDF Views: 138




  • Text Mining Customer Reviews for Aspect-Based Restaurant Rating

Abstract Views: 276  |  PDF Views: 138

Authors

Jovelyn C. Cuizon
University of San Jose-Recoletos, Cebu City, Philippines
Jesserine Lopez
University of San Jose-Recoletos, Cebu City, Philippines
Danica Rose Jones
University of San Jose-Recoletos, Cebu City, Philippines

Abstract


This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.

Keywords


Text Mining, Sentiment Analysis, Natural Language Processing, Aspect-Based Scoring.

References